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Cited by 254Ethics and Social Impacts of AIAdvanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

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Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy

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Yang, Kaiyu, Qinami, Klint, Fei-Fei, Li, Deng, Jia, Russakovsky, Olga·2020·Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
DOI 10.1145/3351095.3375709
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Abstract

Computer vision technology is being used by many but remains representative of only a few. People have reported misbehavior of computer vision models, including offensive prediction results and lower performance for underrepresented groups. Current computer vision models are typically developed using datasets consisting of manually annotated images or videos; the data and label distributions in these datasets are critical to the models' behavior. In this paper, we examine ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods. We consider three key factors within the person subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology: (1) the stagnant concept vocabulary of WordNet, (2) the attempt at exhaustive illustration of all categories with images, and (3) the inequality of representation in the images within concepts. We seek to illuminate the root causes of these concerns and take the first steps to mitigate them constructively.

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Metadata

Authors
Yang, K., Qinami, K., Fei-Fei, L., Deng, J., & Russakovsky, O.
Affiliations
  • Princeton University (US)
  • Stanford University (US)
ORCID
Venue
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Publisher
ACM
Published
2020-01-27
Pages
547-558
DOI
10.1145/3351095.3375709
OpenAlex
W4288083803
Cited by
254
References
50
Citations by year
2026: 13 · 2025: 37 · 2024: 47 · 2023: 48 · 2022: 58 · 2021: 42 · 2020: 9
FWCI
40.71
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Center for Hierarchical Manufacturing, National Science Foundation (1763642)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
ImageNet, underrepresented groups, WordNet, label distribution, data bias

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